OpenRaiser/Intern-Atlas huggingface dataset:
Semi-Supervised Semantic Segmentation With Region Relevance
Venue: arXiv cs.CV 2023
Year: 2023

Semi-supervised semantic segmentation aims to learn from a small amount of labeled data and plenty of unlabeled ones for the segmentation task. The most common approach is to generate pseudo-labels for unlabeled images to augment the training data. However, the noisy pseudo-labels will lead to cumulative classification errors and aggravate the local inconsistency in prediction. This paper proposes a Region Relevance Network (RRN) to alleviate the problem mentioned above. Specifically, we first introduce a local pseudo-label filtering module that leverages discriminator networks to assess the accuracy of the pseudo-label at the region level. A local selection loss is proposed to mitigate the negative impact of wrong pseudo-labels in consistency regularization training. In addition, we propose a dynamic region-loss correction module, which takes the merit of network diversity to further rate the reliability of pseudo-labels and correct the convergence direction of the segmentation network with a dynamic region loss. Extensive experiments are conducted on PASCAL VOC 2012 and Cityscapes datasets with varying amounts of labeled data, demonstrating that our proposed approach achieves state-of-the-art performance compared to current counterparts.
Computing the optimal error exponential function for fixed-length lossy coding in discrete memoryless sources
Venue: arXiv cs.IT 2023
Year: 2023

The error exponent of fixed-length lossy source coding was established by Marton. Ahlswede showed that this exponent can be discontinuous at a rate $R$, depending on the probability distribution $P$ of the given information source and the distortion measure $d(x,y)$. The reason for the discontinuity in the error exponent is that there exists $(d,Δ)$ such that the rate-distortion function $R(Δ|P)$ is neither concave nor quasi-concave with respect to $P$. Arimoto's algorithm for computing the error exponent in lossy source coding is based on Blahut's parametric representation of the error exponent. However, Blahut's parametric representation is a lower convex envelope of Marton's exponent, and the two do not generally agree. The contribution of this paper is to provide a parametric representation that perfectly matches with the inverse function of Marton's exponent, thus avoiding the problem of the rate-distortion function being non-convex with respect to $P$. The optimal distribution for fixed parameters can be obtained using Arimoto's algorithm. Performing a nonconvex optimization over the parameters successfully yields the inverse function of Marton's exponent.
System III: Learning with Domain Knowledge for Safety Constraints
Venue: arXiv cs.LG 2023
Year: 2023

Reinforcement learning agents naturally learn from extensive exploration. Exploration is costly and can be unsafe in $\textit{safety-critical}$ domains. This paper proposes a novel framework for incorporating domain knowledge to help guide safe exploration and boost sample efficiency. Previous approaches impose constraints, such as regularisation parameters in neural networks, that rely on large sample sets and often are not suitable for safety-critical domains where agents should almost always avoid unsafe actions. In our approach, called $\textit{System III}$, which is inspired by psychologists' notions of the brain's $\textit{System I}$ and $\textit{System II}$, we represent domain expert knowledge of safety in form of first-order logic. We evaluate the satisfaction of these constraints via p-norms in state vector space. In our formulation, constraints are analogous to hazards, objects, and regions of state that have to be avoided during exploration. We evaluated the effectiveness of the proposed method on OpenAI's Gym and Safety-Gym environments. In all tasks, including classic Control and Safety Games, we show that our approach results in safer exploration and sample efficiency.
Segment Anything in Non-Euclidean Domains: Challenges and Opportunities
Venue: arXiv cs.CV 2023
Year: 2023

The recent work known as Segment Anything (SA) has made significant strides in pushing the boundaries of semantic segmentation into the era of foundation models. The impact of SA has sparked extremely active discussions and ushered in an encouraging new wave of developing foundation models for the diverse tasks in the Euclidean domain, such as object detection and image inpainting. Despite the promising advances led by SA, the concept has yet to be extended to the non-Euclidean graph domain. In this paper, we explore a novel Segment Non-Euclidean Anything (SNA) paradigm that strives to develop foundation models that can handle the diverse range of graph data within the non-Euclidean domain, seeking to expand the scope of SA and lay the groundwork for future research in this direction. To achieve this goal, we begin by discussing the recent achievements in foundation models associated with SA. We then shed light on the unique challenges that arise when applying the SA concept to graph analysis, which involves understanding the differences between the Euclidean and non-Euclidean domains from both the data and task perspectives. Motivated by these observations, we present several preliminary solutions to tackle the challenges of SNA and detail their corresponding limitations, along with several potential directions to pave the way for future SNA research. Experiments on five Open Graph Benchmark (OGB) datasets across various tasks, including graph property classification and regression, as well as multi-label prediction, demonstrate that the performance of the naive SNA solutions has considerable room for improvement, pointing towards a promising avenue for future exploration of Graph General Intelligence.
Globally Consistent Normal Orientation for Point Clouds by Regularizing the Winding-Number Field
Venue: arXiv cs.GR 2023
Year: 2023

Estimating normals with globally consistent orientations for a raw point cloud has many downstream geometry processing applications. Despite tremendous efforts in the past decades, it remains challenging to deal with an unoriented point cloud with various imperfections, particularly in the presence of data sparsity coupled with nearby gaps or thin-walled structures. In this paper, we propose a smooth objective function to characterize the requirements of an acceptable winding-number field, which allows one to find the globally consistent normal orientations starting from a set of completely random normals. By taking the vertices of the Voronoi diagram of the point cloud as examination points, we consider the following three requirements: (1) the winding number is either 0 or 1, (2) the occurrences of 1 and the occurrences of 0 are balanced around the point cloud, and (3) the normals align with the outside Voronoi poles as much as possible. Extensive experimental results show that our method outperforms the existing approaches, especially in handling sparse and noisy point clouds, as well as shapes with complex geometry/topology.
Using Alternation Direction Method of Multipliers to Enhance robots Calibration Accuracy based on Multi-Planal Constraints
Venue: arXiv cs.RO 2023
Year: 2023

With the widespread application of industrial robots, the problem of absolute positioning accuracy becomes increasingly prominent. To ensure the working state of the robots, researchers commonly adopt calibration techniques to improve its accuracy. However, an industrial robot's working space is mostly restricted in real working environments, making the collected samples fail in covering the actual working space to result in the overall migration data. To address this vital issue, this work proposes a novel industrial robot calibrator that integrates a measurement configurations selection (MCS) method and an alternation-direction-method-of-multipliers with multiple planes constraints (AMPC) algorithm into its working process, whose ideas are three-fold: a) selecting a group of optimal measurement configurations based on the observability index to suppress the measurement noises, b) developing an AMPC algorithm that evidently enhances the calibration accuracy and suppresses the long-tail convergence, and c) proposing an industrial robot calibration algorithm that incorporates MCS and AMPC to optimize an industrial robot's kinematic parameters efficiently. For validating its performance, a public-available dataset (HRS-P) is established on an HRS-JR680 industrial robot. Extensive experimental results demonstrate that the proposed calibrator outperforms several state-of-the-art models in calibration accuracy.
Evaluating Digital Library Search Systems by using Formal Process Modelling
Venue: arXiv cs.DL 2023
Year: 2023

Evaluations of digital library information systems are typically centred on users correctly, efficiently, and quickly performing predefined tasks. Additionally, users generally enjoy working with the evaluated system, and completed questionnaires show an interface's excellent user experience. However, such evaluations do not explicitly consider comparing or connecting user-specific information-seeking behaviour with digital library system capabilities and thus overlook actual user needs or further system requirements. We aim to close this gap by introducing the usage of formalisations of users' task conduction strategies to compare their information needs with the capabilities of such information systems. We observe users' strategies in scope of expert finding and paper search. We propose and investigate using the business process model notation to formalise task conduction strategies and the SchenQL digital library interface as an example system. We conduct interviews in a qualitative evaluation with 13 participants from various backgrounds from which we derive models. We discovered that the formalisations are suitable and helpful to mirror the strategies back to users and to compare users' ideal task conductions with capabilities of information systems. We conclude using formal models for qualitative digital library studies being a suitable mean to identify current limitations and depict users' task conduction strategies. Our published dataset containing the evaluation data can be reused to investigate other digital library systems' fit for depicting users' ideal task solutions.
Alternating (In)Dependence-Friendly Logic
Venue: arXiv cs.LO 2023
Year: 2023

Hintikka and Sandu originally proposed Independence Friendly Logic (IF) as a first-order logic of imperfect information to describe game-theoretic phenomena underlying the semantics of natural language. The logic allows for expressing independence constraints among quantified variables, in a similar vein to Henkin quantifiers, and has a nice game-theoretic semantics in terms of imperfect information games. However, the IF semantics exhibits some limitations. It treats the players asymmetrically, considering only one of the two players as having imperfect information when evaluating truth, resp., falsity, of a sentence. In addition, the truth and falsity of sentences coincide with the existence of a uniform winning strategy for one of the two players in the semantic imperfect information game. As a consequence, IF does admit undetermined sentences, which are neither true nor false, thus failing the law of excluded middle. In this paper, we investigate an extension of IF, called Alternating Dependence/Independence Friendly Logic (ADIF), tailored to overcome these limitations. To this end, we introduce a novel compositional semantics, generalising the one based on trumps proposed by Hodges for IF. The new semantics (i) allows for meaningfully restricting both players at the same time, (ii) enjoys the property of game-theoretic determinacy, (iii) recovers the law of excluded middle for sentences, and (iv) grants ADIF the full descriptive power of Second Order Logic. We also provide an equivalent Herbrand-Skolem semantics and a game-theoretic semantics for the prenex fragment of ADIF, the latter being defined in terms of a determined infinite-duration game that precisely captures the other two semantics on finite structures.
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning
Venue: arXiv cs.LG 2023
Year: 2023

Graph contrastive learning defines a contrastive task to pull similar instances close and push dissimilar instances away. It learns discriminative node embeddings without supervised labels, which has aroused increasing attention in the past few years. Nevertheless, existing methods of graph contrastive learning ignore the differences between diverse semantics existed in graphs, which learn coarse-grained node embeddings and lead to sub-optimal performances on downstream tasks. To bridge this gap, we propose a novel Fine-grained Semantics enhanced Graph Contrastive Learning (FSGCL) in this paper. Concretely, FSGCL first introduces a motif-based graph construction, which employs graph motifs to extract diverse semantics existed in graphs from the perspective of input data. Then, the semantic-level contrastive task is explored to further enhance the utilization of fine-grained semantics from the perspective of model training. Experiments on five real-world datasets demonstrate the superiority of our proposed FSGCL over state-of-the-art methods. To make the results reproducible, we will make our codes public on GitHub after this paper is accepted.
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization
Venue: arXiv cs.LG 2023
Year: 2023

Nowadays, algorithms with fast convergence, small memory footprints, and low per-iteration complexity are particularly favorable for artificial intelligence applications. In this paper, we propose a doubly stochastic algorithm with a novel accelerating multi-momentum technique to solve large scale empirical risk minimization problem for learning tasks. While enjoying a provably superior convergence rate, in each iteration, such algorithm only accesses a mini batch of samples and meanwhile updates a small block of variable coordinates, which substantially reduces the amount of memory reference when both the massive sample size and ultra-high dimensionality are involved. Empirical studies on huge scale datasets are conducted to illustrate the efficiency of our method in practice.
Nuances are the Key: Unlocking ChatGPT to Find Failure-Inducing Tests with Differential Prompting
Venue: arXiv cs.SE 2023
Year: 2023

Automatically detecting software failures is an important task and a longstanding challenge. It requires finding failure-inducing test cases whose test input can trigger the software's fault, and constructing an automated oracle to detect the software's incorrect behaviors. Recent advancement of large language models (LLMs) motivates us to study how far this challenge can be addressed by ChatGPT, a state-of-the-art LLM. Unfortunately, our study shows that ChatGPT has a low probability (28.8%) of finding correct failure-inducing test cases for buggy programs. A possible reason is that finding failure-inducing test cases requires analyzing the subtle code differences between a buggy program and its correct version. When these two versions have similar syntax, ChatGPT is weak at recognizing subtle code differences. Our insight is that ChatGPT's performance can be substantially enhanced when ChatGPT is guided to focus on the subtle code difference. We have an interesting observation that ChatGPT is effective in inferring the intended behaviors of a buggy program. The intended behavior can be leveraged to synthesize programs, in order to make the subtle code difference between a buggy program and its correct version (i.e., the synthesized program) explicit. Driven by this observation, we propose a novel approach that synergistically combines ChatGPT and differential testing to find failure-inducing test cases. We evaluate our approach on Quixbugs (a benchmark of buggy programs), and compare it with state-of-the-art baselines, including direct use of ChatGPT and Pynguin. The experimental result shows that our approach has a much higher probability (77.8%) of finding correct failure-inducing test cases, 2.7X as the best baseline.
Informative Data Selection with Uncertainty for Multi-modal Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

Noise has always been nonnegligible trouble in object detection by creating confusion in model reasoning, thereby reducing the informativeness of the data. It can lead to inaccurate recognition due to the shift in the observed pattern, that requires a robust generalization of the models. To implement a general vision model, we need to develop deep learning models that can adaptively select valid information from multi-modal data. This is mainly based on two reasons. Multi-modal learning can break through the inherent defects of single-modal data, and adaptive information selection can reduce chaos in multi-modal data. To tackle this problem, we propose a universal uncertainty-aware multi-modal fusion model. It adopts a multi-pipeline loosely coupled architecture to combine the features and results from point clouds and images. To quantify the correlation in multi-modal information, we model the uncertainty, as the inverse of data information, in different modalities and embed it in the bounding box generation. In this way, our model reduces the randomness in fusion and generates reliable output. Moreover, we conducted a completed investigation on the KITTI 2D object detection dataset and its derived dirty data. Our fusion model is proven to resist severe noise interference like Gaussian, motion blur, and frost, with only slight degradation. The experiment results demonstrate the benefits of our adaptive fusion. Our analysis on the robustness of multi-modal fusion will provide further insights for future research.
Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions
Venue: arXiv cs.LG 2023
Year: 2023

Limited availability of representative time-to-failure (TTF) trajectories either limits the performance of deep learning (DL)-based approaches on remaining useful life (RUL) prediction in practice or even precludes their application. Generating synthetic data that is physically plausible is a promising way to tackle this challenge. In this study, a novel hybrid framework combining the controlled physics-informed data generation approach with a deep learning-based prediction model for prognostics is proposed. In the proposed framework, a new controlled physics-informed generative adversarial network (CPI-GAN) is developed to generate synthetic degradation trajectories that are physically interpretable and diverse. Five basic physics constraints are proposed as the controllable settings in the generator. A physics-informed loss function with penalty is designed as the regularization term, which ensures that the changing trend of system health state recorded in the synthetic data is consistent with the underlying physical laws. Then, the generated synthetic data is used as input of the DL-based prediction model to obtain the RUL estimations. The proposed framework is evaluated based on new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), a turbofan engine prognostics dataset where a limited avail-ability of TTF trajectories is assumed. The experimental results demonstrate that the proposed framework is able to generate synthetic TTF trajectories that are consistent with underlying degradation trends. The generated trajectories enable to significantly improve the accuracy of RUL predictions.
Automatized marine vessel monitoring from sentinel-1 data using convolution neural network
Venue: arXiv cs.CV 2023
Year: 2023

The advancement of multi-channel synthetic aperture radar (SAR) system is considered as an upgraded technology for surveillance activities. SAR sensors onboard provide data for coastal ocean surveillance and a view of the oceanic surface features. Vessel monitoring has earlier been performed using Constant False Alarm Rate (CFAR) algorithm which is not a smart technique as it lacks decision-making capabilities, therefore we introduce wavelet transformation-based Convolution Neural Network approach to recognize objects from SAR images during the heavy naval traffic, which corresponds to the numerous object detection. The utilized information comprises Sentinel-1 SAR-C dual-polarization data acquisitions over the western coastal zones of India and with help of the proposed technique we have obtained 95.46% detection accuracy. Utilizing this model can automatize the monitoring of naval objects and recognition of foreign maritime intruders.
Images Within Images? A Multi-image Paradigm with Novel Key-Value Graph Oriented Steganography
Venue: arXiv cs.MM 2023
Year: 2023

Steganographic methods have been in the limelight of research and development for concealing secret data within a cover media without being noticed through general visualization. The Least Significant Bits (LSBs) of 8-bit color code for the RGB image arises the possibility of replacing the last two bits with the bits of the encrypted message. Several procedures have been developed to hide an image within another image however in most cases the payload image has to be within the accommodatable range of the cover image and very little literature have shown methods to hide multiple images within multiple images. This paper presents a novel approach to split the image into JSON styled dictionary of key-value pairs and using a metadata graph to locate different parts and positions of the payload images in the entire cluster of cover images. The model could be easily used in the real world scenario for privately sharing secret data over public communication channels without being noticed.
Analysis of Recent Trends in Face Recognition Systems
Venue: arXiv cs.CV 2023
Year: 2023

With the tremendous advancements in face recognition technology, face modality has been widely recognized as a significant biometric identifier in establishing a person's identity rather than any other biometric trait like fingerprints that require contact sensors. However, due to inter-class similarities and intra-class variations, face recognition systems generate false match and false non-match errors respectively. Recent research focuses on improving the robustness of extracted features and the pre-processing algorithms to enhance recognition accuracy. Since face recognition has been extensively used for several applications ranging from law enforcement to surveillance systems, the accuracy and performance of face recognition must be the finest. In this paper various face recognition systems are discussed and analysed like RPRV, LWKPCA, SVM Model, LTrP based SPM and a deep learning framework for recognising images from CCTV. All these face recognition methods, their implementations and performance evaluations are compared to derive the best outcome for future developmental works.
Robust and differentially private stochastic linear bandits
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, we study the stochastic linear bandit problem under the additional requirements of differential privacy, robustness and batched observations. In particular, we assume an adversary randomly chooses a constant fraction of the observed rewards in each batch, replacing them with arbitrary numbers. We present differentially private and robust variants of the arm elimination algorithm using logarithmic batch queries under two privacy models and provide regret bounds in both settings. In the first model, every reward in each round is reported by a potentially different client, which reduces to standard local differential privacy (LDP). In the second model, every action is "owned" by a different client, who may aggregate the rewards over multiple queries and privatize the aggregate response instead. To the best of our knowledge, our algorithms are the first simultaneously providing differential privacy and adversarial robustness in the stochastic linear bandits problem.